ggvis (version 0.4.1)

dplyr-ggvis: Dplyr verbs for ggvis.

Description

Applying a dplyr verb to a ggvis object creates a reactive transformation: whenever the underlying data changes the transformation will be recomputed.

Usage

groups.ggvis(x)
group_by_.ggvis(.data, ..., .dots, add = FALSE)
ungroup.ggvis(x)
summarise_.ggvis(.data, ..., .dots)
mutate_.ggvis(.data, ..., .dots)
arrange_.ggvis(.data, ..., .dots)
select_.ggvis(.data, ..., .dots)
filter_.ggvis(.data, ..., .dots)
distinct_.ggvis(.data, ..., .dots)
slice_.ggvis(.data, ..., .dots)
rename_.ggvis(.data, ..., .dots)
transmute_.ggvis(.data, ..., .dots)
groups.reactive(x)
ungroup.reactive(x)
group_by_.reactive(.data, ..., .dots, add = FALSE)
summarise_.reactive(.data, ..., .dots)
mutate_.reactive(.data, ..., .dots)
arrange_.reactive(.data, ..., .dots)
select_.reactive(.data, ..., .dots)
filter_.reactive(.data, ..., .dots)
distinct_.reactive(.data, ..., .dots)
slice_.reactive(.data, ..., .dots)
rename_.reactive(.data, ..., .dots)
transmute_.reactive(.data, ..., .dots)

Non-standard evaluation

Both dplyr and shiny do non-standard evaluation, so to help each package figure out when it should evaluate its code, reactive components in these functions must be wrapped in eval().

Examples

library(dplyr)
base <- mtcars %>% ggvis(~mpg, ~cyl) %>% layer_points()
base %>% group_by(cyl) %>% summarise(mpg = mean(mpg)) %>%
  layer_points(fill := "red", size := 100)

base %>% filter(mpg > 25) %>% layer_points(fill := "red")

base %>% mutate(cyl = jitter(cyl)) %>% layer_points(fill := "red")

# Dynamically restrict range using filter
mtcars %>% ggvis(~disp, ~mpg) %>%
   filter(cyl > eval(input_slider(0, 10))) %>%
   layer_points()

# Dynamically compute box-cox transformation with mutate
bc <- function(x, lambda) {
  if (abs(lambda) < 1e-6) log(x) else (x ^ lambda - 1) / lambda
}
bc_slider <- input_slider(-2, 2, 1, step = 0.1)
mtcars %>%
 ggvis(~disp, ~mpg) %>%
 mutate(disp = bc(disp, eval(bc_slider))) %>%
 layer_points()